verticapy.vDataFrame.acf¶

Calculates the correlations between the specified vDataColumn and its various time lags. This function is particularly useful for time series analysis and forecasting as it helps uncover relationships between data points at different time intervals. Understanding these correlations can be vital for making predictions and gaining insights into temporal data patterns.

Parameters¶

column: str

Input vDataColumn used to compute the Auto Correlation Plot.

ts: str

TS (Time Series) vDataColumn used to order the data. It can be of type date or a numerical vDataColumn.

by: SQLColumns, optional

vDataColumns used in the partition.

p: int | list, optional

Int equal to the maximum number of lag to consider during the computation or List of the different lags to include during the computation. p must be positive or a list of positive integers.

unit: str, optional

Unit used to compute the lags.

rows:
Natural lags.

else:
Any time unit. For example, you can write ‘hour’ to compute the hours lags or ‘day’ to compute the days lags.

method: str, optional

Method used to compute the correlation.

pearson:
Pearson’s correlation coefficient (linear).

spearman:
Spearman’s correlation coefficient (monotonic - rank based).

spearmanD:
Spearman’s correlation coefficient using the DENSE RANK function instead of the RANK function.

kendall:
Kendall’s correlation coefficient (similar trends). The method computes the Tau-B coefficient.

Warning

This method uses a CROSS JOIN during computation and is therefore computationally expensive at O(n * n), where n is the total count of the vDataFrame.

cramer:
Cramer’s V (correlation between categories).

biserial:
Biserial Point (correlation between binaries and a numericals).

confidence: bool, optional

If set to True, the confidence band width is drawn.

alpha: float, optional

Significance Level. Probability to accept H0. Only used to compute the confidence band width.

show: bool, optional

If set to True, the Plotting object is returned.

kind: str, optional

ACF Type.

bar:
Classical Autocorrelation Plot using bars.

heatmap:
Draws the ACF heatmap.

line:
Draws the ACF using a Line Plot.

mround: int, optional

Round the coefficient using the input number of digits. It is used only to display the ACF Matrix (kind must be set to ‘heatmap’).

chart: PlottingObject, optional

The chart object used to plot.

**style_kwargs

Any optional parameter to pass to the plotting functions.

Returns¶

obj: Plotting Object.

Examples¶

Import the amazon dataset from VerticaPy.

from verticapy.datasets import load_amazon

data = load_amazon()

	📅 date Date	Abc state Varchar(32)	123 number Integer
1	1998-01-01	AMAPÁ	0
2	1998-01-01	AMAZONAS	0
3	1998-01-01	DISTRITO FEDERAL	0
4	1998-01-01	ESPÍRITO SANTO	0
5	1998-01-01	MARANHÃO	0
6	1998-01-01	PARANÁ	0
7	1998-01-01	PIAUÍ	0
8	1998-01-01	RORAIMA	0
9	1998-01-01	SERGIPE	0
10	1998-01-01	SÃO PAULO	0
11	1998-02-01	GOIÁS	0
12	1998-02-01	MATO GROSSO DO SUL	0
13	1998-02-01	MINAS GERAIS	0
14	1998-02-01	PARAÍBA	0
15	1998-02-01	SANTA CATARINA	0
16	1998-02-01	SÃO PAULO	0
17	1998-03-01	AMAPÁ	0
18	1998-03-01	BAHIA	0
19	1998-03-01	MATO GROSSO DO SUL	0
20	1998-03-01	PARÁ	0
21	1998-03-01	PERNAMBUCO	0
22	1998-03-01	RIO GRANDE DO SUL	0
23	1998-04-01	CEARÁ	0
24	1998-04-01	PARANÁ	0
25	1998-04-01	PARAÍBA	0
26	1998-04-01	PARÁ	0
27	1998-05-01	ALAGOAS	0
28	1998-05-01	AMAPÁ	0
29	1998-05-01	MARANHÃO	0
30	1998-05-01	MATO GROSSO DO SUL	0
31	1998-05-01	RIO GRANDE DO SUL	0
32	1998-05-01	TOCANTINS	0
33	1998-06-01	ESPÍRITO SANTO	6
34	1998-06-01	RIO DE JANEIRO	3
35	1998-06-01	RIO GRANDE DO NORTE	1
36	1998-06-01	SÃO PAULO	451
37	1998-07-01	ESPÍRITO SANTO	37
38	1998-07-01	MARANHÃO	274
39	1998-07-01	MATO GROSSO	360
40	1998-07-01	MATO GROSSO DO SUL	3712
41	1998-07-01	PARAÍBA	0
42	1998-07-01	PARÁ	638
43	1998-07-01	RONDÔNIA	365
44	1998-07-01	SÃO PAULO	596
45	1998-08-01	ALAGOAS	1
46	1998-08-01	BAHIA	815
47	1998-08-01	DISTRITO FEDERAL	48
48	1998-08-01	ESPÍRITO SANTO	38
49	1998-08-01	MARANHÃO	1176
50	1998-08-01	MATO GROSSO	228
51	1998-08-01	MINAS GERAIS	875
52	1998-08-01	PIAUÍ	711
53	1998-08-01	RIO GRANDE DO SUL	9
54	1998-08-01	RORAIMA	0
55	1998-08-01	SERGIPE	0
56	1998-09-01	AMAPÁ	20
57	1998-09-01	DISTRITO FEDERAL	33
58	1998-09-01	PIAUÍ	1991
59	1998-09-01	RORAIMA	2
60	1998-10-01	AMAZONAS	83
61	1998-10-01	GOIÁS	1034
62	1998-10-01	MATO GROSSO	576
63	1998-10-01	PARAÍBA	179
64	1998-10-01	PARÁ	3665
65	1998-10-01	PIAUÍ	2586
66	1998-10-01	SERGIPE	0
67	1998-11-01	ALAGOAS	19
68	1998-11-01	AMAPÁ	131
69	1998-11-01	CEARÁ	575
70	1998-11-01	DISTRITO FEDERAL	0
71	1998-11-01	MARANHÃO	2237
72	1998-11-01	RIO DE JANEIRO	6
73	1998-11-01	RIO GRANDE DO SUL	28
74	1998-11-01	SÃO PAULO	488
75	1998-12-01	BAHIA	82
76	1998-12-01	MARANHÃO	1399
77	1998-12-01	MATO GROSSO	100
78	1998-12-01	PARAÍBA	51
79	1998-12-01	PERNAMBUCO	59
80	1998-12-01	RIO DE JANEIRO	1
81	1998-12-01	RONDÔNIA	33
82	1998-12-01	TOCANTINS	9
83	1999-01-01	ALAGOAS	58
84	1999-01-01	GOIÁS	14
85	1999-01-01	MATO GROSSO	239
86	1999-01-01	MINAS GERAIS	36
87	1999-01-01	PARÁ	87
88	1999-01-01	PERNAMBUCO	102
89	1999-01-01	RONDÔNIA	1
90	1999-01-01	SÃO PAULO	7
91	1999-01-01	TOCANTINS	36
92	1999-02-01	ACRE	0
93	1999-02-01	CEARÁ	16
94	1999-02-01	MATO GROSSO	69
95	1999-02-01	MATO GROSSO DO SUL	28
96	1999-02-01	PERNAMBUCO	13
97	1999-02-01	RONDÔNIA	1
98	1999-02-01	SANTA CATARINA	2
99	1999-02-01	TOCANTINS	1
100	1999-03-01	AMAPÁ	2

Rows: 1-100 | Columns: 3

Draw the ACF Plot.

data.acf(
    column = "number",
    ts = "date",
    by = "state",
    method = "pearson",
    p = 24,
)

For more examples, please look at the Auto-Correlation Plot page of the Chart Gallery.